인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Objective: The purpose of this study is to identify optimal algorithm for emotion recognition which classify three different emotional states (happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion reconition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. During three different emotional stimuli are presented to participants, ANS responses(EDA, SKT, ECG, Respiration, and PPG) as physiological signals were measured for 1 minute as baseline and for 1-1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state. Participants assessed the induced emotion on emotional assessment scale after emotional stimuli presentation. Analysis for emotion classification were done by linear discriminant analysis (SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron (MLP) using substracting baseline values from the emotional state. Results: The emotional stimuli had 96% validity and 5.8 effectiveness on average. The result of linear discriminant analysis using physiological signals showed that an accuracy of three different emotions classification was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study identified that three emotions were classified by linear discriminant analysis using various physiological features. Future study is needed to obtain stability and reliablity of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion reecognition.
본문·목차
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최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2013-530-001644704
